In this paper, we propose a novel multi-scale contour instance segmentation framework for identifying suspicious items using X-ray scans. Unlike standard models that employ region-based or keypoint-based techniques to generate multiple boxes around objects [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, we propose to derive proposals according to the hierarchy of the regions defined by the contours.
The insight driving this approach is that contours are the most reliable cue in the X-ray scans due to the lack of surface texture. For example, the occluded items exhibit different transitional patterns based upon their orientation, contrast, and intensity. We try to amplify and exploit this information through the multi-scale scan decomposition, which boosts the proposed framework's capacity for detecting the underlying contraband data in the presence of clutter. Furthermore, we are also motivated by the fact that organic material's suspicious items show only their outlines in the X-ray scans [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}.
To summarize, the main features of this paper are:
